






"""simple docstring"""





from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
  from ..image_utils import load_image


if is_torch_available():
  import torch

  from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

_SCREAMING_SNAKE_CASE   :							int          =		logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE   :							Any          =		Dict[str, Any]
_SCREAMING_SNAKE_CASE   :							Optional[int]          =		List[Prediction]






@add_end_docstrings(__snake_case						)
class 	a		(   __snake_case						):






    def __init__(			self	:				Tuple			,		*__SCREAMING_SNAKE_CASE	:				Optional[int]			,		**__SCREAMING_SNAKE_CASE	:				Optional[int]   )	->						Optional[Any]:
      super().__init__(*__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )

      if self.framework == "tf":
        raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''   )

      requires_backends(self			,		'vision'   )
      self.check_model_type(
          dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()   )   )






    def   UpperCamelCase			(			self	:				List[Any]			,		**__SCREAMING_SNAKE_CASE	:				Optional[int]   )	->						str:
      lowerCamelCase_       	=		{}
      if "threshold" in kwargs:
        lowerCamelCase_       	=		kwargs['threshold']
      return {}, {}, postprocess_kwargs






    def __call__(			self	:				Dict			,		*__SCREAMING_SNAKE_CASE	:				List[Any]			,		**__SCREAMING_SNAKE_CASE	:				int   )	->						Union[Predictions, List[Prediction]]:
      return super().__call__(*__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				int   )	->						Tuple:
      lowerCamelCase_       	=		load_image(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		torch.IntTensor([[image.height, image.width]]   )
      lowerCamelCase_       	=		self.image_processor(images=[image]			,		return_tensors='pt'   )
      if self.tokenizer is not None:
        lowerCamelCase_       	=		self.tokenizer(text=inputs['words']			,		boxes=inputs['boxes']			,		return_tensors='pt'   )
      lowerCamelCase_       	=		target_size
      return inputs






    def   UpperCamelCase			(			self	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]   )	->						List[str]:
      lowerCamelCase_       	=		model_inputs.pop('target_size'   )
      lowerCamelCase_       	=		self.model(**__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		outputs.__class__({'target_size': target_size, **outputs}   )
      if self.tokenizer is not None:
        lowerCamelCase_       	=		model_inputs['bbox']
      return model_outputs






    def   UpperCamelCase			(			self	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				Any=0.9   )	->						Tuple:
      lowerCamelCase_       	=		model_outputs['target_size']
      if self.tokenizer is not None:
        # This is a LayoutLMForTokenClassification variant.
        # The OCR got the boxes and the model classified the words.
        lowerCamelCase_	,      lowerCamelCase_       	=		target_size[0].tolist()

        def unnormalize(__SCREAMING_SNAKE_CASE	:				str   ):
          return self._get_bounding_box(
              torch.Tensor(
                  [
                      (width * bbox[0] / 1000),
                      (height * bbox[1] / 1000),
                      (width * bbox[2] / 1000),
                      (height * bbox[3] / 1000),
                  ]   )   )

        lowerCamelCase_	,      lowerCamelCase_       	=		model_outputs['logits'].squeeze(0   ).softmax(dim=-1   ).max(dim=-1   )
        lowerCamelCase_       	=		[self.model.config.idalabel[prediction] for prediction in classes.tolist()]
        lowerCamelCase_       	=		[unnormalize(__SCREAMING_SNAKE_CASE   ) for bbox in model_outputs['bbox'].squeeze(0   )]
        lowerCamelCase_       	=		['score', 'label', 'box']
        lowerCamelCase_       	=		[dict(zip(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )   ) for vals in zip(scores.tolist()			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ) if vals[0] > threshold]
      else:
        # This is a regular ForObjectDetectionModel
        lowerCamelCase_       	=		self.image_processor.post_process_object_detection(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
        lowerCamelCase_       	=		raw_annotations[0]
        lowerCamelCase_       	=		raw_annotation['scores']
        lowerCamelCase_       	=		raw_annotation['labels']
        lowerCamelCase_       	=		raw_annotation['boxes']

        lowerCamelCase_       	=		scores.tolist()
        lowerCamelCase_       	=		[self.model.config.idalabel[label.item()] for label in labels]
        lowerCamelCase_       	=		[self._get_bounding_box(__SCREAMING_SNAKE_CASE   ) for box in boxes]

        # {"scores": [...], ...} --> [{"score":x, ...}, ...]
        lowerCamelCase_       	=		['score', 'label', 'box']
        lowerCamelCase_       	=		[
            dict(zip(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )   )
            for vals in zip(raw_annotation['scores']			,		raw_annotation['labels']			,		raw_annotation['boxes']   )
        ]

      return annotation






    def   UpperCamelCase			(			self	:				Dict			,		__SCREAMING_SNAKE_CASE	:				"torch.Tensor"   )	->						Dict[str, int]:
      if self.framework != "pt":
        raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.'   )
      lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_       	=		box.int().tolist()
      lowerCamelCase_       	=		{
          'xmin': xmin,
          'ymin': ymin,
          'xmax': xmax,
          'ymax': ymax,
      }
      return bbox












"""simple docstring"""





from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest






@require_tokenizers
class 	a		(   __snake_case						):
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					DistilBertTokenizer
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					DistilBertTokenizerFast
    SCREAMING_SNAKE_CASE		:	int		=					True






    @slow
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						int:
      lowerCamelCase_       	=		DistilBertTokenizer.from_pretrained('distilbert-base-uncased'   )

      lowerCamelCase_       	=		tokenizer.encode('sequence builders'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.encode('multi-sequence build'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
      assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
          tokenizer.sep_token_id
      ]




